Module 1: Machine Learning

Overview

This week focuses on Machine Learning fundamentals and practices. We’ll cover essential concepts and tools used in modern ML workflows, with a particular emphasis on PyTorch and its ecosystem.

Instructor

Muhan Zhang, PKU

Topics Covered

• PyTorch basics
  • Installation (Linux-only)
  • Core libraries and functions
• CNN/Transformer training
  • Training in PyTorch
  • Hyperparameter tuning
• Introduction to Weights & Biases (WandB)
• Reproducibility in ML experiments

Assignments

Practice Assignment: Using PyTorch, complete a simple classification task. Use WandB to record the entire experiment process, ensuring reproducibility.

Written Assignment: Write a one-page report (written in LaTeX) in the GitHub Classroom-assigned repo, outlining the implementation process of the Practice Assignment part 1. Explain the required steps and specify which PyTorch functions are used.

• Assignment: Machine Learning

Additional Resources

• PyTorch Documentation
• Weights & Biases Documentation
• Reproducibility in Learning

Notes

• Ensure you have a Linux environment set up for PyTorch installation. If you’re using a different OS, consider using a virtual machine or WSL (Windows Subsystem for Linux).
• Familiarize yourself with WandB before starting the assignment; it will be crucial for tracking your experiments.
• Submit your assignments on GitHub Classroom.